Complex data sets may include chaotic, quasi-chaotic, and/or semi-random data sets and may be generated by a variety of industrial processes. As an example, airborne particle count data from cleanroom environments may be complex in nature. This airborne particle count data may remain relatively stable, with just a baseline noise level, for significant periods of time and then increase unexpectedly to elevated levels that may be unacceptable within the cleanroom environment.
The increase in airborne particle counts within the cleanroom environment may be caused by a variety of factors. As an example, filters that may be utilized to cleanse air that is circulated within the cleanroom environment may degrade, may decay, and/or may become clogged. As another example, a “dirty” object may be moved into the cleanroom environment and may shed particles within the cleanroom environment. As yet another example, human error may cause doors, windows, and/or other access points to the cleanroom environment to be left open, permitting particles to enter the cleanroom environment. As another example, workers may shed particles within the cleanroom environment.
The above-described sources of increased airborne particle counts may be quasi-random in nature and/or may be difficult to predict. At the same time, an elevated airborne particle count may be detrimental to work that is performed within the cleanroom environment and/or to the quality of a part that is fabricated within the cleanroom environment. With this in mind, simply observing, or measuring, an increased airborne particle count within the cleanroom environment and responding after-the-fact may be costly and/or time-consuming to a manufacturing process that is performed within the cleanroom environment. Thus, there exists a need for improved predictive analysis of complex data sets and for systems and methods that include and/or utilize the predictive analysis.